As single-cell technologies evolved over years, diverse single-cell atlas datasets have been rapidly accumulated. Integrative analyses harmonizing such datasets provide opportunities for gaining deep biological insights. In this talk, we present a computational approach developed for fast and accurate integration of large-scale single-cell atlases. Our method incorporates generative adversarial networks and auto-encoder structures into a unified framework. Through integration of numerous datasets, we show that our method outperforms other state-of-the-art methods in terms of scalability and accuracy.

4 May 2022
4:00pm - 5:00pm
Where
https://hkust.zoom.us/j/92441893149 (Passcode: 538242)
Speakers/Performers
Miss Jia ZHAO
Organizer(S)
Department of Mathematics
Contact/Enquiries
Payment Details
Audience
Alumni, Faculty and staff, PG students, UG students
Language(s)
English
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